The fundamental challenge in ILP is to efficiently search a large hypothesis space (the set of all hypotheses) for a solution (a hypothesis that correctly generalises the examples).
Divide-and-conquer (D&C) approaches, such as TILDE [1]}, divide the examples into disjoint sets and then search for a hypothesis for each set, similar to decision tree learners [2]}.
Separate-and-conquer (S&C) approaches, such as Progol [3]} and Aleph [4]}, search for a hypothesis that generalises a subset of the examples, separate these examples, and then search for more rules to add to the hypothesis to generalise the remaining examples.
